Optical coherence tomography (OCT) provides significant advantages of high-resolution (approaching the histopathology level) real-time imaging of tissues without use of contrast agents. Based on these advantages, the microstructural features of tumors can be visualized and detected intra-operatively. However, it is still not clinically accepted for tumor margin delineation due to poor specificity and accuracy. In contrast, Raman spectroscopy (RS) can obtain tissue information at the molecular level, but does not provide real-time imaging capability. Therefore, combining OCT and RS could provide synergy. To this end, we present a tissue analysis and classification method using both the slope of OCT intensity signal versus depth and the principle components from the RS spectrum as the indicators for tissue characterization. Our pilot experiments were performed on mouse kidneys, livers, and small intestines. The prediction accuracy with five-fold cross validation of the method has been evaluated by support vector machine method. The results demonstrate that RS can effectively improve tissue classification compared to OCT alone. Next, we demonstrate that the boundary between myxoid liposarcoma and normal fat which is easily identifiable both Raman and OCT. In cases where structural images are indistinguishable, for example, in normal fat and well differentiated liposarcoma (WDLS) or gastrointestinal sarcoma tumor (GIST) and Myxoma, distinct molecular spectra have been obtained. The results suggest RS can effectively complement OCT to tumor boundary demarcation with high specificity.